{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# NYC Taxi data in Winter and Summer\r\n",
        "\r\n",
        "Refer to the [Data dictionary](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) to learn more about the columns that have been provided.\r\n"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [
        "#Install the pandas library\r\n",
        "!pip install pandas"
      ],
      "outputs": [],
      "metadata": {
        "scrolled": true
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "source": [
        "import pandas as pd\r\n",
        "\r\n",
        "path = '../../data/taxi.csv'\r\n",
        "\r\n",
        "#Load the csv file into a dataframe\r\n",
        "df = pd.read_csv(path)\r\n",
        "\r\n",
        "#Print the dataframe\r\n",
        "print(df)\r\n"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
            "0         2.0  2019-07-15 16:27:53   2019-07-15 16:44:21              3.0   \n",
            "1         2.0  2019-07-17 20:26:35   2019-07-17 20:40:09              6.0   \n",
            "2         2.0  2019-07-06 16:01:08   2019-07-06 16:10:25              1.0   \n",
            "3         1.0  2019-07-18 22:32:23   2019-07-18 22:35:08              1.0   \n",
            "4         2.0  2019-07-19 14:54:29   2019-07-19 15:19:08              1.0   \n",
            "..        ...                  ...                   ...              ...   \n",
            "195       2.0  2019-01-18 08:42:15   2019-01-18 08:56:57              1.0   \n",
            "196       1.0  2019-01-19 04:34:45   2019-01-19 04:43:44              1.0   \n",
            "197       2.0  2019-01-05 10:37:39   2019-01-05 10:42:03              1.0   \n",
            "198       2.0  2019-01-23 10:36:29   2019-01-23 10:44:34              2.0   \n",
            "199       2.0  2019-01-30 06:55:58   2019-01-30 07:07:02              5.0   \n",
            "\n",
            "     trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
            "0             2.02         1.0                  N           186           233   \n",
            "1             1.59         1.0                  N           141           161   \n",
            "2             1.69         1.0                  N           246           249   \n",
            "3             0.90         1.0                  N           229           141   \n",
            "4             4.79         1.0                  N           237           107   \n",
            "..             ...         ...                ...           ...           ...   \n",
            "195           1.18         1.0                  N            43           237   \n",
            "196           2.30         1.0                  N           148           234   \n",
            "197           0.83         1.0                  N           237           263   \n",
            "198           1.12         1.0                  N           144           113   \n",
            "199           2.41         1.0                  N           209           107   \n",
            "\n",
            "     payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
            "0             1.0         12.0    1.0      0.5        4.08           0.0   \n",
            "1             2.0         10.0    0.5      0.5        0.00           0.0   \n",
            "2             2.0          8.5    0.0      0.5        0.00           0.0   \n",
            "3             1.0          4.5    3.0      0.5        1.65           0.0   \n",
            "4             1.0         19.5    0.0      0.5        5.70           0.0   \n",
            "..            ...          ...    ...      ...         ...           ...   \n",
            "195           1.0         10.0    0.0      0.5        2.16           0.0   \n",
            "196           1.0          9.5    0.5      0.5        2.15           0.0   \n",
            "197           1.0          5.0    0.0      0.5        1.16           0.0   \n",
            "198           2.0          7.0    0.0      0.5        0.00           0.0   \n",
            "199           1.0         10.5    0.0      0.5        1.00           0.0   \n",
            "\n",
            "     improvement_surcharge  total_amount  congestion_surcharge  \n",
            "0                      0.3         20.38                   2.5  \n",
            "1                      0.3         13.80                   2.5  \n",
            "2                      0.3         11.80                   2.5  \n",
            "3                      0.3          9.95                   2.5  \n",
            "4                      0.3         28.50                   2.5  \n",
            "..                     ...           ...                   ...  \n",
            "195                    0.3         12.96                   0.0  \n",
            "196                    0.3         12.95                   0.0  \n",
            "197                    0.3          6.96                   0.0  \n",
            "198                    0.3          7.80                   0.0  \n",
            "199                    0.3         12.30                   0.0  \n",
            "\n",
            "[200 rows x 18 columns]\n"
          ]
        }
      ],
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Use the cells below to do your own Exploratory Data Analysis"
      ],
      "metadata": {}
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "source": [],
      "outputs": [],
      "metadata": {}
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3.9.7 64-bit ('venv': venv)"
    },
    "language_info": {
      "mimetype": "text/x-python",
      "name": "python",
      "pygments_lexer": "ipython3",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "version": "3.9.7",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "name": "04-nyc-taxi-join-weather-in-pandas",
    "notebookId": 1709144033725344,
    "interpreter": {
      "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}